{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(784, 100),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(100, 10),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "\n",
    "        self.loss_function = nn.MSELoss()\n",
    "\n",
    "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.02)\n",
    "\n",
    "        # record progress\n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        return self.model(inputs)\n",
    "\n",
    "    def train(self, inputs, targets):\n",
    "        outputs = self.forward(inputs)\n",
    "        loss = self.loss_function(outputs, targets)\n",
    "\n",
    "        self.optimiser.zero_grad() # set gradient to 0\n",
    "        loss.backward() # backward loss and calculate gradient\n",
    "        self.optimiser.step() # update parameters\n",
    "\n",
    "        # record progress\n",
    "        self.counter += 1\n",
    "        if self.counter % 10 == 0:\n",
    "            self.progress.append(loss.item())\n",
    "        if self.counter % 10000 == 0:\n",
    "            print(f'counter: {self.counter}')\n",
    "\n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(18, 8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistDataset(Dataset):\n",
    "    def __init__(self, csv_file):\n",
    "        self.data_df = pd.read_csv(csv_file, header=None)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_df)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        label = self.data_df.iloc[index, 0]\n",
    "        target = torch.zeros(10)\n",
    "        target[label] = 1.0\n",
    "        img_values = torch.FloatTensor(self.data_df.iloc[index, 1:].values) / 255.0\n",
    "        return label, img_values, target\n",
    "\n",
    "    def plot_image(self, index):\n",
    "        arr = self.data_df.iloc[index, 1:].values.reshape(28, 28)\n",
    "        plt.title(f'label = {self.data_df.iloc[index, 0]}')\n",
    "        plt.imshow(arr, interpolation='none', cmap='Blues')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dataset = MnistDataset('mnist_dataset/mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist_dataset.plot_image(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,\n",
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       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.8824, 0.9922, 0.9059, 0.8353, 0.8353, 0.4824, 0.0627, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.6745, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.7451,\n",
       "         0.2471, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0078, 0.4549, 0.2824, 0.4863, 0.8196, 0.9922,\n",
       "         0.9922, 0.5529, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0980,\n",
       "         0.8588, 0.9922, 0.8078, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.4078, 0.9647, 0.9922, 0.0196, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.8353, 0.9922, 0.0196, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.1020, 0.8863, 0.9922, 0.0196, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.5176, 0.9922, 0.8196, 0.0118, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3059, 0.9922, 0.3373, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
       "         0.0000]),\n",
       " tensor([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_dataset[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n"
     ]
    }
   ],
   "source": [
    "C = Classifier()\n",
    "for label, image_data_tensor, target_tensor in mnist_dataset:\n",
    "    C.train(image_data_tensor, target_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epoch: 0\n",
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n",
      "training epoch: 1\n",
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n",
      "training epoch: 2\n",
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n",
      "Wall time: 3min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "epochs = 3\n",
    "for i in range(epochs):\n",
    "    print(f'training epoch: {i}')\n",
    "    C = Classifier()\n",
    "    for label, image_data_tensor, target_tensor in mnist_dataset:\n",
    "        C.train(image_data_tensor, target_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('ai')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0cb0046d74bfb5a9fee6ebb85b9850d762e40d6b321d5d9a0aa0313eefa3c5b4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
